Data Scientist vs. Data Analyst: Which Should You Hire? ⚖️
- Engineering, Dev & IT
Both fields deal in data, but which is the best choice for your business? Here’s what you need to know about data scientists versus data analysts.
As a business grows, so does the amount of information it generates. This data—transaction reports, end-of-month statements, bank account statements, and more—provides a vital record of a company’s performance and a potential look into its future. But for those with little experience interpreting data, it might be information overload.
Depending on what the data is for, there are a couple of related (but quite distinct) experts who can help. Understanding data scientists versus data analysts is step one to determining exactly what your business needs.
What is a data scientist? 🧑🔬
A data scientist is a professional focused on understanding and manipulating data. Data science analyzes data sets using artificial intelligence or predictive modeling software to find uses for data. Data scientists are primarily back-end-focused, prioritizing sorting and understanding information to make analysis easier.
So, what does a data scientist do with all that info? Here are a few applications:
- Creating algorithms to answer data-related questions. Data science looks for new ways to interact with large data sets, coming up with questions that may not have been asked before and finding the answers.
- Developing new ways to understand and manipulate data. Our understanding of data is constantly shifting, thanks to the work of data scientists. They design new programs and explore new ways of interpreting information.
- Gathering and refining raw data. Not all data is useful. One of the primary jobs of a data scientist is data mining and sorting vast swaths of raw data into usable sets, all while removing unnecessary material.
- Preparing data and tools for an analyst. The most direct link between the two professions is that data scientists are often responsible for the tools a data analyst uses.
What is a data analyst? 🕵️
Using data management tools, such as Excel or Structured Query Language (SQL), a data analyst combs through large amounts of information to find answers to questions or problems. If a business’s sales have fallen, a data analytics expert will use their tools to search for the cause and locate potential solutions to bolster sales.
Regarding what a data analyst does, their skillset usually includes the following:
- Using SQLs to gather data. A data analyst uses SQL to access and manipulate databases. This helps them collect information from primary and secondary sources.
- Recognizing patterns and trends across data sets. A data analyst must analyze and translate data. This analysis employs data modeling to answer specific questions a business may have about growth, income, or demographics.
- Crafting and presenting data-driven solutions. Communication skills are paramount in this profession. A data analyst must not only perform analysis on data sets but also deliver that analysis to stakeholders to help them make business decisions.
- Simplifying data results for general audiences. Both data analysts and data scientists have high-level expertise in understanding data, but data analysts are often tasked with boiling down their results for a non-expert audience.
What’s the difference? 🔬
Although these careers are intimately linked, they serve distinct functions. Here’s how to tell the difference between data analyst and data scientist roles:
1. Manipulation versus application.
A data scientist is driven by the need to process and manipulate data. They look for novel ways to interact with large data sets. On the other hand, an analyst uses their skills and the tools created by data scientists to identify uses for specific data sets.
2. Back end versus front end.
Typically, a scientist works solely with the data, using programming languages such as R, Java, or Python. An analyst works predominantly on the front end, preparing data for stakeholder presentations or written reports.
3. Big data versus small data.
Scientists often take enormous collections of data and streamline them, cleaning out junk data so that AI or analysts can parse the information more easily. Analysis focuses on smaller subsets of data, usually tackling particular questions or concerns.
4. Program creation versus program use.
Perhaps the most significant difference between the two roles is that a scientist creates programs for data manipulation, while an analyst uses those programs to interpret individual data sets.
5. Graduate versus undergraduate.
Both data scientist and data analyst roles require collegiate computer science education. Analysis generally calls for a bachelor’s degree, although some online courses offer certification to experienced professionals without a degree. On the other hand, data scientists often hold an advanced degree, such as a Master’s or Doctorate. Their degree may be in data science, information technology, statistics, or another relevant field.
Who should I hire? 🤝
The decision to hire either a data scientist or a data analyst depends on a company’s needs. A company should hire a data scientist if it is inundated with big data sets and needs help understanding and manipulating the numbers. Using “extract, transform, load” (ETL) pipelines, a scientist can place large amounts of data in a “data warehouse,” ready to be analyzed by whatever new tool they create. They can also build structures to help organize vast amounts of data.
Hiring a data analyst is the best choice if a company’s decision-makers need to understand figures related to sales, marketing, social media, or other information. A data analyst’s job involves implementing visualization tools on individual data sets. The statistical models and frameworks presented by an analyst inform the company’s decision-making process. Successful data analysts have a range of soft skills, including public speaking or technical writing, that help clearly communicate their findings.
Crunch the numbers with Contra 🧮
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We can help you craft the perfect opportunity to entice a freelance scientist or analyst. Start by perusing our best practices for working with Independents. Regardless of your data desires, Contra’s Discover feed has an Independent that fits the bill.